Speaker:

Xiao-Ping (Steven) Zhang, Ph.D., M.B.A., P.Eng.,SMIEEE

Professor, Director of Communication and Signal Processing Applications Laboratory (CASPAL)

Program Director of Graduate Studies, Department of Electrical & Computer Engineering

Ryerson University, Canada

Title: Adaptive Noise Reduction Based on Wavelet Thresholding

Date & Time: Tuesday 29^th April, 15:00

Venue: C505

Abstract: Wavelet shrinkage/thresholding has attracted more attention recently due to its simplicity and effectiveness in noise removal.  In this talk, adaptive wavelet thresholding and its performance are systematically presented.  A system framework of wavelet thresholding for adaptive noise reduction is established, which is named thresholding neural network (TNN).  New types of thresholding functions are created to serve as activation functions.  Unlike the standard thresholding functions, the new thresholding functions are infinitely differentiable.  By using the new thresholding functions, some gradient-based learning algorithms become possible or more effective. General optimal performances of TNNs are analyzed.  Gradient-based adaptive learning algorithms are presented to seek the optimal solution for noise reduction.  The algorithms include supervised and unsupervised batch learning as well as supervised and unsupervised stochastic learning.  It is indicated that the TNN with the stochastic learning algorithms can be used as a novel nonlinear adaptive filter.  Numerical results show that the TNN is very effective in finding the optimal solutions of thresholding methods in an MSE sense and usually outperforms other noise reduction methods.  Especially, it is shown that the TNN based nonlinear adaptive filtering outperforms the conventional linear adaptive filtering in both optimal solution and learning performance.

Short Bio：Xiao-Ping (Steven) Zhang received the B.S. and Ph.D. degrees from Tsinghua University, in 1992 and 1996, respectively, all in electronic engineering. He holds an MBA in Finance, Economics and Entrepreneurship with Honors from the University of Chicago Booth School of Business.

Since Fall 2000, he has been with the Department of Electrical and Computer Engineering, Ryerson University, where he is now Professor, Director of Communication and Signal Processing Applications Laboratory (CASPAL). He has served as Program Director of Graduate Studies. He is cross appointed to the Finance Department at the Ted Rogers School of Management at Ryerson University. Prior to joining Ryerson, he was a Senior DSP Engineer at SAM Technology, Inc., San Francisco, and a consultant at San Francisco Brain Research Institute. He held research and teaching positions at the Communication Research Laboratory, McMaster University, and worked as a postdoctoral fellow at the Beckman Institute, the University of Illinois at Urbana-Champaign, and the University of Texas, San Antonio. His research interests include statistical signal processing, multimedia retrieval and video content analysis, sensor networks and electronic systems, computational intelligence, and applications in bioinformatics, finance, and marketing. He is a frequent consultant for biotech companies and investment firms. He is cofounder and CEO for EidoSearch, an Ontario based company offering a content-based search and analysis engine for financial data.

Dr. Zhang is a registered Professional Engineer in Ontario, Canada, a Senior Member of IEEE and a member of Beta Gamma Sigma Honor Society. He is the publicity chair for ICME'06 and program chair for ICIC'05 and ICIC'10. He served as guest editor for Multimedia Tools and Applications, and the International Journal of Semantic Computing. He is a tutorial speaker in ACMMM2011, ISCAS2013, ICIP2013 and ICASSP2014. He is currently an Associate Editor for IEEE Transactions on Signal Processing, IEEE Transactions on Multimedia, IEEE Signal Processing letters and for Journal of Multimedia.